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| from transformers import pipeline | |
| import gradio as gr | |
| # Load pre-trained pipelines | |
| try: | |
| summarizer = pipeline("summarization", model="sshleifer/distilbart-cnn-12-6") | |
| ner = pipeline("ner", model="Davlan/bert-base-multilingual-cased-ner-hrl", aggregation_strategy="simple") | |
| except Exception as e: | |
| summarizer = None | |
| ner = None | |
| print("Error loading models:", e) | |
| # Nigerian law reference (basic keyword-to-punishment mapping) | |
| crime_punishment_map = { | |
| "theft": {"law": "Criminal Code Act, Section 390", "punishment": "Up to 3 years imprisonment"}, | |
| "armed robbery": {"law": "Robbery and Firearms Act, Section 1", "punishment": "Death penalty or life imprisonment"}, | |
| "internet fraud": {"law": "Cybercrime Act, 2015", "punishment": "Minimum of 7 years imprisonment"}, | |
| "rape": {"law": "Criminal Law of Lagos State, Section 260", "punishment": "Life imprisonment"}, | |
| "murder": {"law": "Criminal Code Act, Section 319", "punishment": "Death penalty"}, | |
| "assault": {"law": "Criminal Code Act, Section 351", "punishment": "1 year imprisonment"} | |
| } | |
| def classify_crime(text): | |
| text = text.lower() | |
| for crime in crime_punishment_map: | |
| if crime in text: | |
| return crime, crime_punishment_map[crime] | |
| return "unknown", { | |
| "law": "N/A", | |
| "punishment": "No specific punishment found. Manual review required." | |
| } | |
| # Main analysis function with full error handling | |
| def analyze_text(text): | |
| try: | |
| if not text.strip(): | |
| return "No text provided.", [], {"Crime Type": "N/A", "Applicable Law": "N/A", "Recommended Punishment": "N/A"} | |
| summary = summarizer(text, max_length=80, min_length=30, do_sample=False)[0].get("summary_text", "Summary failed.") | |
| entities = ner(text) | |
| crime_type, law_info = classify_crime(text) | |
| return summary, entities, { | |
| "Crime Type": crime_type.title() if crime_type != "unknown" else "Unknown", | |
| "Applicable Law": law_info["law"], | |
| "Recommended Punishment": law_info["punishment"] | |
| } | |
| except Exception as e: | |
| return f"⚠️ An error occurred: {str(e)}", [], { | |
| "Crime Type": "Error", | |
| "Applicable Law": "Error", | |
| "Recommended Punishment": "Error" | |
| } | |
| # Launch app | |
| gr.Interface( | |
| fn=analyze_text, | |
| inputs=gr.Textbox(lines=12, label="Paste Criminal Case Text"), | |
| outputs=[ | |
| gr.Textbox(label="Summary"), | |
| gr.JSON(label="Extracted Entities"), | |
| gr.JSON(label="Legal Analysis / Recommended Punishment") | |
| ], | |
| title="JusticeAI - Legal Case Analyzer", | |
| description="Paste any criminal case report. This AI will summarize it, extract important entities, and recommend the legal punishment based on Nigerian law." | |
| ).launch() |